Introduction

Starbucks’ gargantuan $26 billion of revenue in 2019 is resounding evidence that Americans, above everything, love their coffee. They prove it time and time again, most recently during the current pandemic: some, unable to relinquish their daily ‘bucks, waited hours on end in long drive-thru lines to get their fix when coffee shops were closed for in-person operations.

It is no surprise, then, that Starbucks owns more than 8,000 stores across the US, and continues to grow everyday. These sheer numbers are reflective of a company that is about more than just coffee, having a significant influence on US society and culture with (often controversial) initiatives such as removing religious references from holiday-themed cups. In addition, Starbucks is known for treating their employees extremely well by providing health coverage, tuition coverage, and 401(k) plans, which again reinforces their highly-regarded brand.

As such, the astounding popularity of the chain in the country and the prospect of further expansion raises important questions. What can the current distribution of Starbucks stores tell us about societal factors across the country? Which factors should the brand consider when expanding into new locations? How can companies like Starbucks, which pride themselves in a positive social and environmental outlook, incorporate such values into their corporate strategy - especially in the current pandemic?

Distribution of Starbucks Across the World and US

TEXT EXPLAINING INTRODUCTION VISUAL OF STARBUCKS LOCATIONS Text explaining map, transition from current clusters of starbucks to clusters of potential locations.

General Process Towards Selecting New Location: 1. Find States 2. Find Counties

Data

Collected data from 2 Sources: Twitter and Kaggle…..

Clustering by State

Paragraph about how we went about determining clusters of states, and explaining why we want to use clusters and explaining factors we use

State-Level Factors

  • Consumer Sentiments
  • Average Income
  • Average Unemployment Rate
  • Number of Starbucks

Cosumer Sentiments Across US

Explain how we collected data through twitter and used colleciton of words to find proportion of positive and negative words of tweets containing the word “starbucks” and had a geo-location, used 18,000 tweets.

Income, Unemployment, and Starbucks Data

Explain data came from a kaggle and a county level, which we summarized into states

State-Level Clusters

Explain cluster analysis and elbow plot helped to determine 5 clusters were needed

Visualization of Cluster

So we chose 5 clusters based on this plot, and the 5 clusters are shown on this visualization:

Cluster Characteristics

Explain we can see characteristics of each cluster through this matrix. Explain each cluster’s unique properties

Final List of States

Then, we calculated the center within each center, finding the states within each cluster that best represented each cluster by finding the state with the miminal distance between each characterstic’s value and itself. From this analysis we concluded the final 5 states, each representative of their own cluster, are:

  • Virginia
  • Vermont
  • Nevada
  • Wisconsin
  • California

Clustering by County

County-Level Factors

  • Average Income
  • Average Unemployment Rate
  • Number of Starbucks

Step-by Step Process

  1. Cluster Analysis
  2. Creating clusters based on appropriate number of centers
  3. Determine the most suitable cluster of counties

Determining Amount of Clusters for Each State

Virginia

Cluster Analysis

Cluster characteristics, explain why we chose cluster 4

## NULL

Vermont

Cluster Analysis

Visualizing clusters

Cluster characteristics, explain why we chose cluster 1

Nevada

Cluster Analysis

Visualizing clusters

Cluster characteristics, explain why we chose cluster 4

Wisconsin

Cluster Analysis

Visualizing clusters

Cluster characteristics, explain why we chose cluster 5

California

Cluster Analysis

Visualizing clusters

Cluster characteristics, explain why we chose cluster 1

Final Recommendations

Summarize overall process and thinking and impact of starbucks. Show the data table of all important counties from clusters we chose:

## # A tibble: 20 x 4
##    County           `Avg. Income` `Num. Starbucks` `Avg. Unemployment Rate`
##    <chr>                    <dbl>            <dbl>                    <dbl>
##  1 "Glenn "                53148                 0                    12.0 
##  2 "Modoc "                43340.                0                    10.4 
##  3 "Plumas "               64925.                0                    11.3 
##  4 "Yuba "                 55172.                0                    12.3 
##  5 "Clark "                55938                 0                     5.72
##  6 "Esmeralda "            43837                 0                     6.44
##  7 "White Pine "           24166                 0                     6.27
##  8 "Chittenden "           83284.                0                     3.39
##  9 "Brunswick "            35873                 0                     7.47
## 10 "Cumberland "           46083                 0                     6.30
## 11 "Grayson "              32076                 0                     7.06
## 12 "Greensville "          33193.                0                     5.82
## 13 "Halifax "              29411                 0                     8.66
## 14 "Lee "                  31678.                0                     7.17
## 15 "Northampton "          39718.                0                     6.74
## 16 "Prince Edward "        54858                 0                     6.60
## 17 "Scott "                46810                 0                     5.71
## 18 "Smyth "                39226                 0                     8.15
## 19 "Outagamie "            85460.                0                     4.52
## 20 "Sheboygan "            78002                 0                     4.53

Limitations and Conclusion

Paragraph here

Citations

List of important packages/data sets